Transcriptomics for Clinical and Experimental Biology Research: Hang on a Seq.
Tanner StokesHaoning Howard CenPhilipp KapranovIain J GallagherAndrew A PitsillidesClaude-Henry VolmarWilliam E KrausJames D JohnsonStuart M PhillipsClaes WahlestedtJames A TimmonsPublished in: Advanced genetics (Hoboken, N.J.) (2023)
Sequencing the human genome empowers translational medicine, facilitating transcriptome-wide molecular diagnosis, pathway biology, and drug repositioning. Initially, microarrays are used to study the bulk transcriptome; but now short-read RNA sequencing (RNA-seq) predominates. Positioned as a superior technology, that makes the discovery of novel transcripts routine, most RNA-seq analyses are in fact modeled on the known transcriptome. Limitations of the RNA-seq methodology have emerged, while the design of, and the analysis strategies applied to, arrays have matured. An equitable comparison between these technologies is provided, highlighting advantages that modern arrays hold over RNA-seq. Array protocols more accurately quantify constitutively expressed protein coding genes across tissue replicates, and are more reliable for studying lower expressed genes. Arrays reveal long noncoding RNAs (lncRNA) are neither sparsely nor lower expressed than protein coding genes. Heterogeneous coverage of constitutively expressed genes observed with RNA-seq, undermines the validity and reproducibility of pathway analyses. The factors driving these observations, many of which are relevant to long-read or single-cell sequencing are discussed. As proposed herein, a reappreciation of bulk transcriptomic methods is required, including wider use of the modern high-density array data-to urgently revise existing anatomical RNA reference atlases and assist with more accurate study of lncRNAs.
Keyphrases
- rna seq
- single cell
- high density
- high throughput
- genome wide
- genome wide identification
- single molecule
- high resolution
- genome wide analysis
- bioinformatics analysis
- dna methylation
- endothelial cells
- amino acid
- gene expression
- healthcare
- clinical practice
- electronic health record
- emergency department
- deep learning
- small molecule
- long noncoding rna
- binding protein
- adverse drug
- artificial intelligence